Border fences threaten movements of large mammals in southwestern China post-COVID-19 pandemic
Bibliographic record
Abstract
Transboundary areas are known for their rich biodiversity, yet experiencing extensive infrastructure development. During the COVID-19 pandemic, continual border fences were constructed in Yunnan Province and Guangxi Zhuang Autonomous Region (hereafter Yunnan and Guangxi), southwestern China, which may pose huge threats to wildlife movements and weren’t dismantled in the post-pandemic era. To assess the extent of border fences and their impacts on the cross-border movements of mammals, we compiled data from government reports and collected locations through field surveys and visual interpretation of satellite imagery. Our findings indicate that at least 2,392 km of border fences were constructed in Yunnan and 517 km in Guangxi, respectively, accounting for 57.74% and 50.69% of their national boundaries. Twelve fence points might influence 53 large mammal species within their distribution areas, with most (73%) experiencing population declines and nearly half (42%) threatened with extinction. Given the prevalence of border fence construction in this biodiversity hotspot, we advocate for wildlife surveys along border lines, the prompt removal of these temporary fences, and the revegetation of deforested areas. These actions will enhance habitat connectivity and facilitate the cross-border movement of animals, which are crucial for transboundary conservation and aligning with the Kunming-Montreal Global Biodiversity Framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".